library(fixest);library(AER)
rm(list=setdiff(ls(), "twd")) 

data<-readRDS(paste(twd,"data/made_data/vreg_wgeos_race.rds",sep=""))


## In person 20
data$in_person_primary_20<-ifelse(data$voting_method_200303 == "P",1,0)
## Abs early 20
data$abs_early_primary_20<-ifelse(data$voting_method_200303 %in% c("A","E"),1,0)
## In person 18
data$in_person_general_18<-ifelse(data$voting_method_181106 == "P",1,0)
## Abs early 18
data$abs_early_general_18<-ifelse(data$voting_method_181106 %in% c("A","E"),1,0)
## Take First Difference
data$diff_in_person<-data$in_person_primary_20-data$in_person_general_18
data$diff_abs_early<-data$abs_early_primary_20-data$abs_early_general_18
### get the minimum of the distance to a consolidated (including super sites)
data$distance_realized_min<-apply(cbind(data$distance_realized,data$distancegov1,data$distancegov2),1,min,na.rm=T)
## Difference between assigned and realized distances (in logs and levels)
data$ln_dist_change<-ifelse(data$moved==1,log(data$distance_realized_min)-log(data$distance_assigned),0)
data$lev_dist_change<-ifelse(data$moved==1,data$distance_realized_min-data$distance_assigned,0)
### Difference between assigned and realized size # of assigned voters (in logs and levels)
data$ln_diff_N<-log(data$realized_N)-log(data$assigned_N)
data$lev_diff_N<-data$realized_N-data$assigned_N

#### Create differences in turnout for all previous elections and run regression ######

## 18-18b

data$in_person_primary_18<-ifelse(data$voting_method_180802== "P",1,0)
data$abs_early_primary_18<-ifelse(data$voting_method_180802 %in% c("A","E"),1,0)

data$diff_in_person_18_18<-data$in_person_general_18-data$in_person_primary_18
data$diff_abs_early_18_18<-data$abs_early_general_18-data$abs_early_primary_18

## 18-16

data$in_person_general_16<-ifelse(data$voting_method_161108== "P",1,0)
data$abs_early_general_16<-ifelse(data$voting_method_161108 %in% c("A","E"),1,0)

data$diff_in_person_18_16<-data$in_person_primary_18-data$in_person_general_16
data$diff_abs_early_18_16<-data$abs_early_primary_18-data$abs_early_general_16

## 16-16a

data$in_person_primary_16a<-ifelse(data$voting_method_160804== "P",1,0)
data$abs_early_primary_16a<-ifelse(data$voting_method_160804 %in% c("A","E"),1,0)

data$diff_in_person_16_16a<-data$in_person_general_16-data$in_person_primary_16a
data$diff_abs_early_16_16a<-data$abs_early_general_16-data$abs_early_primary_16a

## 16a-16b

data$in_person_primary_16b<-ifelse(data$voting_method_160301== "P",1,0)
data$abs_early_primary_16b<-ifelse(data$voting_method_160301 %in% c("A","E"),1,0)


data$diff_in_person_16_16b<-data$in_person_primary_16a-data$in_person_primary_16b
data$diff_abs_early_16_16b<-data$in_person_primary_16a-data$abs_early_primary_16b

## 16b-14

data$in_person_general_14<-ifelse(data$voting_method_141104== "P",1,0)
data$abs_early_general_14<-ifelse(data$voting_method_141104 %in% c("A","E"),1,0)

data$diff_in_person_16_14<-data$in_person_primary_16b-data$in_person_general_14
data$diff_abs_early_16_14<-data$in_person_primary_16b-data$abs_early_general_14

## 14-14a

data$in_person_primary_14<-ifelse(data$voting_method_140807== "P",1,0)
data$abs_early_primary_14<-ifelse(data$voting_method_140807 %in% c("A","E"),1,0)

data$diff_in_person_14_14<-data$in_person_general_14 - data$in_person_primary_14
data$diff_abs_early_14_14<-data$abs_early_general_14 - data$abs_early_primary_14

## 14a-12

data$in_person_general_12<-ifelse(data$voting_method_121106== "P",1,0)
data$abs_early_general_12<-ifelse(data$voting_method_121106 %in% c("A","E"),1,0)

data$diff_in_person_14_12<-data$in_person_primary_14 - data$in_person_general_12
data$diff_abs_early_14_12<-data$abs_early_primary_14 - data$abs_early_general_12

### Make an Figure

par(mfrow=c(1,1))

votes<-c("in_person_primary_20","in_person_general_18","in_person_primary_18","in_person_general_16","in_person_primary_16a","in_person_primary_16b","in_person_general_14","in_person_primary_14","in_person_general_12")

## Average of each group by each election

avg_mov<-avg_cons<-avg_cont<-rep(NA, length(votes))

for(i in 1:length(votes)){
  avg_mov[i]<-mean(data[data$moved==1,votes[i]],na.rm=T)
  avg_cons[i]<-mean(data[data$consolidated==1 & data$moved==0,votes[i]],na.rm=T)
  avg_cont[i]<-mean(data[data$consolidated==0 & data$moved==0,votes[i]],na.rm=T)
}


pdf(paste(twd,"figs/parallel_trends.pdf",sep=""),12,6)


par(mar=c(3,5,2,1))
plot(length(avg_mov):1,avg_mov,type="n",axes=F,xlab="",ylim=c(.05,.225),ylab="E(In Person Turnout)",main="Trends in In-Person Turnout")
grid()
points(length(avg_mov):1,avg_mov,type="b")
points(length(avg_mov):1,avg_cons,type="b",pch=16)
points(length(avg_mov):1,avg_cont,type="b",pch=3)
axis(2,seq(0,.25,by=.05))
axis(1,length(avg_mov):1,lab= rev(c("11/12","8/14","11/14","3/16","8/16","11/16","8/18","11/18","3/20")))
abline(v=8.9,lty=2,col="dark grey")

text(9.1,.185,"Control",cex=.75,font=1)
text(8.9,.1575,"Consolidated",cex=.75,font=1)
text(8.9,.0925,"Reprecincted",cex=.75,font=1)

dev.off()